COMPOSV++: Light Weight Online Signature Verification Framework Through Compound Feature Extraction and Few-Shot Learning

Abstract

Online Signature Verification (OSV) is a systematically used biometric characteristic to endorse the genuineness of a user to access real time applications like healthcare, m-payment, etc. Because OSV frameworks are used in real-time applications and it is difficult to acquire a sufficient number of signature samples from users, they must meet a critical requirement: they must be able to detect skilled and random signature presentation attacks effectively with fewer training signature samples and a faster response time. To meet these needs, we developed a depth wise separable (DWS) convolution-based OSV framework that realizes one/few shot learning in inference phase. In addition to it, we have designed a compound feature extraction technique, which extracts maximum seven features from a set of 100 features in MCYT-100, and 3 features from a set of 47 in case of {SVC, SUSIG} datasets. The framework uses only three to seven features per signature to resist the signature presentation attacks. We have extensively evaluated our framework, by performing thorough experiments with three datasets i.e. MCYT-100, SVC and SUSIG. The model results state of the art EER in all skilled categories of SVC and SUSIG datasets.